Knowledge Mobilization by Provincial Politicians: The United Front against Trafficking in Ontario, Canada
Bibliographic record
Abstract
In recent years, some Canadian provinces have followed the federal government’s intensification of anti-trafficking measures. Ontario is perhaps the most significant in this respect, especially with its introduction of the 2017 Anti-Human Trafficking Act. We set out to investigate debates in the Legislative Assembly of Ontario regarding the then proposed law. Our aim was to understand how politicians and others who presented at the debates mobilized knowledge, and to offer opportunities of resistance to discursive injustice for future policymaking. We engaged in a critical discourse analysis approach to examine how different types of knowledge were applied and reproduced, with a particular focus on explicit and implicit knowledge statements. Through analysis of the debate transcripts, we found a near unified front against a perceived humanitarian crisis that required an urgent punitive and securitized response, based largely in individualistic, moralistic, and colonial notions of “risk” and vulnerability. Our findings uncovered a reliance on strong beliefs and willful silences to narrate a trafficking story that displaced social conditions onto a purportedly immoral sex trade. This conceptualization was advanced through repeated invalidation of sexual labor, with invocations of childhood innocence, thereby hindering the promotion of just and inclusive societies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.049 | 0.019 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".